MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients
MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients
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DOI：
10.1002/mp.15137
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发表时间：
2021-08-06
期刊：
影响因子：
3.8
通讯作者：
Jiang, Xiran
中科院分区：
文献类型：
--
作者：
Ren, Meihong;Yang, Huazhe;Jiang, Xiran
目的：本研究旨在建立和评估基于多参数mri的放射组学，用于表皮生长因子受体（EGFR）突变的术前识别，这对原发性肺腺癌胸椎转移患者的治疗计划具有重要意义。方法：在2016年1月至2019年3月期间，共有110名患者入组，作为主要队列。在2019年7月至2021年4月期间，对52名患者进行了时间无关的验证队列研究。病理诊断为原发性肺腺癌胸椎转移；所有患者均接受了胸椎t1加权（T1W）、T2加权（T2W）和T2加权脂肪抑制（T2FS） MRI扫描。从每个MRI模式中提取和选择手工制作和基于深度学习的特征，并用于构建放射组学签名。开发并比较了各种机器学习分类器。结合受试者工作特征（ROC）、校准和决策曲线分析（DCA），构建综合放射特征和最重要临床因素的临床-放射组学nomogram（临床-放射组学nomogram）来评估预测效果。结果：三种方式联合获得的放射组学特征能有效地对EGFR突变和EGFR野生型患者进行分类，训练组的ROC曲线下面积（AUC）为0.886(95%可信区间[CI]: 0.826-0.947, SEN =0.935, SPE =0.688)，时间独立验证组的AUC为0.803 （95% CI: 0.682- -0.924, SEN = 0.700, SPE = 0.818）。结合放射组学特征和吸烟状况的nomogram在训练队列（AUC = 0.888, 95% CI: 0.849-0.958, SEN = 0.839, SPE = 0.792）和时间无关验证队列（AUC = 0.821, 95% CI: 0.692-0.929, SEN = 0.667, SPE = 0.909）中获得了最好的预测效果。DCA证实了我们的nomogram临床应用潜力。结论：我们的研究证明了基于多参数mri的放射组学在术前预测EGFR突变方面的潜力。该模型可作为一种新的生物标志物，指导原发性肺腺癌胸椎转移患者的个体化治疗策略选择。
Purpose: This study aims to develop and evaluate multi-parametric MRI-based radiomics for preoperative identification of epidermal growth factor receptor (EGFR) mutation, which is important in treatment planning for patients with thoracic spinal metastases from primary lung adenocarcinoma.Methods: A total of 110 patients were enrolled between January 2016 and March 2019 as a primary cohort. A time-independent validation cohort was conducted containing 52 patients consecutively enrolled from July 2019 to April 2021. The patients were pathologically diagnosed with thoracic spinal metastases from primary lung adenocarcinoma; all underwent T1-weighted (T1W), T2- -weighted (T2W), and T2-weighted fat-suppressed (T2FS) MRI scans of the thoracic spinal. Handcrafted and deep learning-based features were extracted and selected from each MRI modality, and used to build the radiomics signature. Various machine learning classifiers were developed and compared. A clinical-radiomics nomogram integrating the combined rad signature and the most important clinical factor was constructed with receiver operating characteristic (ROC), calibration, and decision curves analysis (DCA) to evaluate the prediction performance.Results: The combined radiomics signature derived from the joint of three modalities can effectively classify EGFR mutation and EGFR wild-type patients, with an area under the ROC curve (AUC) of 0.886 (95% confidence interval [CI]: 0.826-0.947, SEN =0.935, SPE =0.688) in the training group and 0.803 (95% CI: 0.682- -0.924, SEN = 0.700, SPE = 0.818) in the time-independent validation group. The nomogram incorporating the combined radiomics signature and smoking status achieved the best prediction performance in the training (AUC = 0.888, 95% CI: 0.849-0.958, SEN = 0.839, SPE = 0.792) and time-independent validation (AUC = 0.821, 95% CI: 0.692-0.929, SEN = 0.667, SPE = 0.909) cohorts. The DCA confirmed potential clinical usefulness of our nomogram.Conclusion: Our study demonstrated the potential of multi-parametric MRI-based radiomics on preoperatively predicting the EGFR mutation. The proposed nomogram model can be considered as a new biomarker to guide the selection of individual treatment strategies for patients with thoracic spinal metastases from primary lung adenocarcinoma.